Remarkable conservation exists between Drosophila and humans at the level of genes and gene functions. For example, there are identifiable fly orthologs for about two-thirds of human disease genes and all of the major signal transduction pathways have been conserved between flies and humans. The availability of RNAi for gene-specific knockdown of mRNA levels has accelerated the pace at which we can undertake the type of molecular genetic analyses that make Drosophila such a powerful system. However, despite the impressive set of tools currently available, locating and/or generating Drosophila disease models can be time consuming, and in most cases, well-characterized loss-of-function alleles or validated RNAi strains are not available. The proposed project would build upon our existing expertise and infrastructure to produce a large collection of in vivo Drosophila RNAi models of human disease, the HuDis-TRiP Resource of Disease Models. This resource will be unique in its large scale, targeting approximately 900 Drosophila orthologs of human disease genes, and in the high level of quality control applied to each RNAi fly stock. For human disease genes for which loss-of-function of the gene is associated with disease state, HuDis-TRiP fly stocks are likely to serve as disease models by mimicking the human disease state at the cell, tissue and/or organism level. Specifically, we propose to: Aim 1, compile a prioritized list of human disease genes using the Online Mendelian Inheritance in Man database and a community nomination process; Aim 2, identify 900 high-confidence fly orthologs of these genes; make two transgenic Drosophila RNAi fly stocks per gene; perform phenotypic characterization, quantitative PCR and rescue to validate the resource; and Aim 3, further characterize the most promising models. Notably, our initial list of conserved disease-associated genes includes genes relevant to nearly all NIH Institutes. In keeping with our commitment to community-based efforts, all RNAi fly stocks will be rapidly transferred to a public stock center and all datasets will be made available through our own database, website (www.flyrnai.org) and other databases. We anticipate that this resource will be widely used by the community, as it will allow scientists to immediately begin their studies with high quality, validated disease models in a powerful genetic system.